ORCID ID

0000-0003-1958-204X

Date of Award

12-2022

Degree Type

Dissertation

Degree Name

Ph.D.

Degree Program

Engineering and Applied Science - Physics

Department

Physics

Major Professor

Juliette Ioup

Second Advisor

James Candy

Third Advisor

Dimitrios Charalampidis

Fourth Advisor

C. Greg Seab

Fifth Advisor

Leszek Malkinski

Abstract

The use of underwater acoustics can be an important component in obtaining information from the oceans of the world. It is desirable (but difficult) to compile an acoustic catalog of sounds emitted by various underwater objects to complement optical catalogs. For example, the current visual catalog for whale tail flukes of large marine mammals (whales) can identify even individual whales from their individual fluke characteristics. However, since sperm whales, Physeter microcephalus, do not fluke up when they dive, they cannot be identified in this manner. A corresponding acoustic catalog for sperm whale clicks could be compiled to identify individual sperm whales. A first step for recorded underwater acoustic data is usually noise removal. In this paper we will be using both Fourier and wavelet methods to remove undesirable noise in our attempt to identify unique sperm whale signals.

The Fourier wavelet based regularized deconvolution (ForWaRD) algorithm is a combination of Fourier deconvolution and wavelet denoising. This computational algorithm uses wavelet thresholding; Weiner Fourier deconvolution; and wavelet decomposition with two different wavelet choices. The methods used in the ForWaRD algorithm have been modified and/or changed in our research to better achieve the goal of identifying individual sperm whales for an acoustic catalog.

The research described here modifies the ForWaRD algorithm to be used with recently acquired broadband underwater acoustic data from sperm whales (primarily females and calves) in the northern Gulf of Mexico. Several additional modifications were needed for the algorithm to yield the best results. Applying wavelet denoising and Fourier deconvolution allows smoothing of the data and separating the signal from the impulse response. Results indicate that the modification of using other methods of Fourier deconvolution improves the percent error in data reconstruction with our underwater acoustic data. Other modifications were made to the type of wavelet thresholding, the wavelet choice improves the quality of the results. The exact modifications to the ForWaRD algorithm and the results will be outlined and described. It will be shown how our proposed modifications produce superior results by comparing methods and producing a percent error for each.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

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